This invention pertains to detection of transient events in noise and more particularly is concerned with preprocessing data in combination with an artificial neural network to decide the presence or absence of a non-periodic signal at a given time.
Artificial neural networks are powerful nonlinear processors, characteristics that make them particularly suitable for decision making applications. Neural networks can be trained by example. For a neural network to be trained to recognize events in class A (signals of interest) and reject events in class B (background noise), it is sufficient to construct training sets of representative examples from Class A and Class B. These training sets are used to adjust the neural network (by means of the selection of values for internal weights for connection between neural network nodes) so that it recognizes the two training sets. Because neural networks can generalize their recognition capabilities, the neural network will distinguish between the entire Class A and Class B, if the training sets are sufficiently representative.
Artificial neural networks also have the ability to make decisions based on large amounts of redundant information. Nevertheless, neural networks typically perform optimally if the input information has been preprocessed in a way so as to enhance the distinctive features of the data.